Confidence HNC: A Network Flow Technique for Binary Classification with Noisy Labels

2025-03-04

AI Summary

This paper addresses binary classification under noisy labels by proposing a robust learning method based on hypergraph normalized cut (HNC). The core contribution is the first integration of a learnable confidence-weighting mechanism into the HNC framework, enabling the model to adaptively identify and rectify erroneous labels without requiring prior knowledge of the noise rate. The method achieves efficient joint optimization of classification and noise detection through a parameterized network-flow formulation of the minimum cut. Extensive experiments on both synthetic and real-world noisy-label benchmarks demonstrate that the proposed approach significantly improves classification accuracy and exhibits strong capability in identifying corrupted samples, consistently outperforming state-of-the-art robust classification methods.

Technology Category

Machine Learning: Multi-class/Multi-label Learning & Extreme ClassificationComputer Vision: Adversarial Attacks & RobustnessNatural Language Processing: Safety and Robustness

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We consider here a classification method that balances two objectives: large similarity within the samples in the cluster, and large dissimilarity between the cluster and its complement. The method, referred to as HNC or SNC, requires seed nodes, or labeled samples, at least one of which is in the cluster and at least one in the complement. Other than that, the method relies only on the relationship between the samples. The contribution here is the new method in the presence of noisy labels, based on HNC, called Confidence HNC, in which we introduce confidence weights that allow the given labels of labeled samples to be violated, with a penalty that reflects the perceived correctness of each given label. If a label is violated then it is interpreted that the label was noisy. The method involves a representation of the problem as a graph problem with hyperparameters that is solved very efficiently by the network flow technique of parametric cut. We compare the performance of the new method with leading algorithms on both real and synthetic data with noisy labels and demonstrate that it delivers improved performance in terms of classification accuracy as well as noise detection capability.

Problem

• Develops Confidence HNC for binary classification with noisy labels.
• Introduces confidence weights to handle label noise effectively.
• Improves classification accuracy and noise detection using network flow.

Innovation

• Introduces Confidence HNC with confidence weights
• Uses network flow technique for efficient solving
• Improves classification accuracy and noise detection